Field
The present disclosure is generally directed to analyzing content for topic guidance, and more specifically to systems and methods for topic guidance using sequence mining of video content.
Related Art
Streaming video content and video-based communication is gaining in popularity, creating large volumes of recorded video content directed to entertainment, education, hobbies, skill development, etc. For example, a massive open online course (MOOC) is a video-based online course aimed at unlimited participation and open access via the Internet that may include participants watching a series of recorded videos for a subject. Institutions, organizations, and content publishers are amassing large databases of recorded video content spanning the full spectrum from one-off how-to videos to full for-profit, professionally produced, multi-course certification programs available via the Internet.
Video-based courses for a particular subject typically include several video segments that each include one or more specific concepts related to the subject. Generally, an instructor or producer selects which specific concepts to include for the course and arranges the topics into the segments. Various factors can impact the design of a course, such as a prescribed curriculum, objectives, and access to materials. For example, teachers generally employ a syllabus to select specific concepts and organize the presentation of the concepts.
A growing number of platforms and instructors have designed video-based courses for common subjects that include diverse arrangements of concepts and teaching methods with differing levels of effectiveness. Conventionally, a viewer (e.g., student, employee, hobbyist, etc.) selects a specific videos series for a subject and completes the series of video segments as designed and organized by the instructor. However, despite the fast-paced growth of educational video content and enthusiastic interest for the convenience and accessibility of the video content, viewers generally struggle to complete video-based courses. For example, a viewer may discover several segments into the series that they are missing a foundational skill, need to refresh prerequisite information, dislike the pace or presenter, etc. Related art studies have shown that video-based courses designed using a one-size-fits-all approach suffers from common attention attrition problems.
Related art systems are not designed to allow viewers to jump between different series of videos to locate relevant topics that contribute to comprehending the particular subject. Typically, when a viewer switches to a different video series for the same subject, the viewer restarts from the beginning of the new series, guesses which segments of the new series might have relevant information, or gives-up completing the subject. Generally, related art systems cause the viewer to re-watch large amounts of redundant or superfluous topics that may be incoherent, confusing, and disconnected with the original video series.
Further, video series are generally designed and organized independent from other video series for the same subject. For example, a video series designed to be a comprehensive study of a subject may be organized very differently from an abridged video series designed by a different instructor or by an unaffiliated institution. Thus, viewers are generally unable to coherently or logically transition between multiple series even though the different series including overlapping topics. For example, online course aimed at unlimited participation and open access are limited by the ability to address a diversity of learner profiles. Improved production and distribution of video content is increasing video-based education and employment training, as well as, spurring life-long learners to pick up new skills. Therefore, tools are needed for viewers to leverage different series of video content related to a common subject.
In the related art, topic detection in text content has been used to group and model similar topics from different sources, for example, different news sources or social-media platforms discussing a common event. In other related art, video recommendation techniques assign categories to video segments and recommend a new video segment that is classified in the same or similar category as the previously viewed video segment. Related art video recommendation tools contribute to viewer attrition and course drop-out rates by recommending disorienting video segments that typically include redundant or superfluous topics.